864 research outputs found
Efficient Spatial Keyword Search in Trajectory Databases
An increasing amount of trajectory data is being annotated with text
descriptions to better capture the semantics associated with locations. The
fusion of spatial locations and text descriptions in trajectories engenders a
new type of top- queries that take into account both aspects. Each
trajectory in consideration consists of a sequence of geo-spatial locations
associated with text descriptions. Given a user location and a
keyword set , a top- query returns trajectories whose text
descriptions cover the keywords and that have the shortest match
distance. To the best of our knowledge, previous research on querying
trajectory databases has focused on trajectory data without any text
description, and no existing work has studied such kind of top- queries on
trajectories. This paper proposes one novel method for efficiently computing
top- trajectories. The method is developed based on a new hybrid index,
cell-keyword conscious B-tree, denoted by \cellbtree, which enables us to
exploit both text relevance and location proximity to facilitate efficient and
effective query processing. The results of our extensive empirical studies with
an implementation of the proposed algorithms on BerkeleyDB demonstrate that our
proposed methods are capable of achieving excellent performance and good
scalability.Comment: 12 page
Representation and Exploitation of Event Sequences
Programa Oficial de Doutoramento en Computación . 5009V01[Abstract]
The Ten Commandments, the thirty best smartphones in the market and
the five most wanted people by the FBI. Our life is ruled by sequences:
thought sequences, number sequences, event sequences. . . a history book
is nothing more than a compilation of events and our favorite film is
just a sequence of scenes. All of them have something in common, it
is possible to acquire relevant information from them. Frequently, by
accumulating some data from the elements of each sequence we may
access hidden information (e.g. the passengers transported by a bus
on a journey is the sum of the passengers who got on in the sequence
of stops made); other times, reordering the elements by any of their
characteristics facilitates the access to the elements of interest (e.g. the
publication of books in 2019 can be ordered chronologically, by author,
by literary genre or even by a combination of characteristics); but it
will always be sought to store them in the smallest space possible.
Thus, this thesis proposes technological solutions for the storage
and subsequent processing of events, focusing specifically on three
fundamental aspects that can be found in any application that needs
to manage them: compressed and dynamic storage, aggregation
or accumulation of elements of the sequence and element sequence
reordering by their different characteristics or dimensions.
The first contribution of this work is a compact structure for the
dynamic compression of event sequences. This structure allows any
sequence to be compressed in a single pass, that is, it is capable of
compressing in real time as elements arrive. This contribution is
a milestone in the world of compression since, to date, this is the
first proposal for a variable-to-variable dynamic compressor for general purpose.
Regarding aggregation, a data warehouse-like proposal is presented
capable of storing information on any characteristic of the events in a
sequence in an aggregated, compact and accessible way. Following the
philosophy of current data warehouses, we avoid repeating cumulative
operations and speed up aggregate queries by preprocessing the
information and keeping it in this separate structure.
Finally, this thesis addresses the problem of indexing event sequences
considering their different characteristics and possible reorderings. A new
approach for simultaneously keeping the elements of a sequence ordered
by different characteristics is presented through compact structures.
Thus, it is possible to consult the information and perform operations
on the elements of the sequence using any possible rearrangement in a
simple and efficient way.[Resumen]
Los diez mandamientos, los treinta mejores móviles del mercado y las
cinco personas más buscadas por el FBI. Nuestra vida está gobernada
por secuencias: secuencias de pensamientos, secuencias de números,
secuencias de eventos. . . un libro de historia no es más que una sucesión
de eventos y nuestra película favorita no es sino una secuencia de
escenas. Todas ellas tienen algo en común, de todas podemos extraer
información relevante. A veces, al acumular algún dato de los elementos
de cada secuencia accedemos a información oculta (p. ej. los viajeros
transportados por un autobús en un trayecto es la suma de los pasajeros
que se subieron en la secuencia de paradas realizadas); otras veces, la
reordenación de los elementos por alguna de sus características facilita
el acceso a los elementos de interés (p. ej. la publicación de obras
literarias en 2019 puede ordenarse cronológicamente, por autor, por
género literario o incluso por una combinación de características); pero
siempre se buscará almacenarlas en el espacio más reducido posible sin
renunciar a su contenido.
Por ello, esta tesis propone soluciones tecnológicas para el almacenamiento
y posterior procesamiento de secuencias, centrándose
concretamente en tres aspectos fundamentales que se pueden encontrar
en cualquier aplicación que precise gestionarlas: el almacenamiento
comprimido y dinámico, la agregación o acumulación de algún dato
sobre los elementos de la secuencia y la reordenación de los elementos
de la secuencia por sus diferentes características o dimensiones.
La primera contribución de este trabajo es una estructura compacta
para la compresión dinámica de secuencias. Esta estructura permite
comprimir cualquier secuencia en una sola pasada, es decir, es capaz de comprimir en tiempo real a medida que llegan los elementos de la
secuencia. Esta aportación es un hito en el mundo de la compresión ya
que, hasta la fecha, es la primera propuesta de un compresor dinámico
“variable to variable” de carácter general.
En cuanto a la agregación, se presenta una propuesta de almacén
de datos capaz de guardar la información acumulada sobre alguna
característica de los eventos de la secuencia de modo compacto y
fácilmente accesible. Siguiendo la filosofía de los actuales almacenes de
datos, el objetivo es evitar repetir operaciones de acumulación y agilizar
las consultas agregadas mediante el preprocesado de la información
manteniéndola en esta estructura.
Por último, esta tesis aborda el problema de la indexación de
secuencias de eventos considerando sus diferentes características y
posibles reordenaciones. Se presenta una nueva forma de mantener
simultáneamente ordenados los elementos de una secuencia por diferentes
características a través de estructuras compactas. Así se permite
consultar la información y realizar operaciones sobre los elementos
de la secuencia usando cualquier posible ordenación de una manera
sencilla y eficiente
Autonomous Navigation of Mobile Robots in Complex Dynamic Environments
Most of the future robots will be mobile, and the main challenge is to develop algorithms for their autonomous navigation as well as for human-robot interactions. The Laboratory for Autonomous Systems and Mobile Robotics (LAMOR) at the Faculty of Electrical Engineering and Computing of the University of Zagreb is involved in the research of such mobile robotic systems, and currently participates in a number of related international and national research projects. This paper addresses the issue of autonomous navigation of mobile robots in complex dynamic environments, providing state of the art of the domain and major LAMOR’s contribution to it. At the end, we present an application example of the autonomous navigation technologies in flexible warehouses, which we have been developing within a Horizon 2020 project SafeLog
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Spatio-temporal map maintenance for extending autonomy in long-term mobile robotic tasks
Working in hazardous environments requires routine inspections in order to meet safety standards. Dangerous quantities of nuclear contamination can exist in infinitesimally small volumes. In order to confidently inspect a nuclear environment for radioactive sources, especially those which emit alpha radiation, technicians must carefully maintain detectors at a consistent velocity and distance from a source. Technicians must also take careful records of which areas have been surveyed or not are important so that no area is left unmonitored. This is a difficult, exhausting task when the coverage area is larger than an office space. An autonomous mobile robotic platform with Complete Coverage Path Planning (CCPP) can reduce dangerous exposure to humans and provide better information for Radiological Control Technicians (RCT). The developed robotic system - or RCTbot - is designed for long-term deployment with little human correction, intervention, or maintenance required. To do this, the RCTbot creates a map of the environment, continually updates it based on multiple sensor inputs, and searches its map for contamination. In nuclear environments, the areas of interest often remain spatially constant throughout the duration of an inspection and are considered temporally static. The RCTbot monitors temporally static environments but adapts to dynamic changes over time. It then uses its sensor data to update and maintain its map so no manual human intervention is necessary. The spatio-temporal map maintenance (STMM) is agnostic to the survey type, so the RCTbot system is viable for application domain other than nuclear.Mechanical Engineerin
NEW METHODS FOR MINING SEQUENTIAL AND TIME SERIES DATA
Data mining is the process of extracting knowledge from large amounts of data. It covers a variety of techniques aimed at discovering diverse types of patterns on the basis of the requirements of the domain. These techniques include association rules mining, classification, cluster analysis and outlier detection. The availability of applications that produce massive amounts of spatial, spatio-temporal (ST) and time series data (TSD) is the rationale for developing specialized techniques to excavate such data. In spatial data mining, the spatial co-location rule problem is different from the association rule problem, since there is no natural notion of transactions in spatial datasets that are embedded in continuous geographic space. Therefore, we have proposed an efficient algorithm (GridClique) to mine interesting spatial co-location patterns (maximal cliques). These patterns are used as the raw transactions for an association rule mining technique to discover complex co-location rules. Our proposal includes certain types of complex relationships – especially negative relationships – in the patterns. The relationships can be obtained from only the maximal clique patterns, which have never been used until now. Our approach is applied on a well-known astronomy dataset obtained from the Sloan Digital Sky Survey (SDSS). ST data is continuously collected and made accessible in the public domain. We present an approach to mine and query large ST data with the aim of finding interesting patterns and understanding the underlying process of data generation. An important class of queries is based on the flock pattern. A flock is a large subset of objects moving along paths close to each other for a predefined time. One approach to processing a “flock query” is to map ST data into high-dimensional space and to reduce the query to a sequence of standard range queries that can be answered using a spatial indexing structure; however, the performance of spatial indexing structures rapidly deteriorates in high-dimensional space. This thesis sets out a preprocessing strategy that uses a random projection to reduce the dimensionality of the transformed space. We use probabilistic arguments to prove the accuracy of the projection and to present experimental results that show the possibility of managing the curse of dimensionality in a ST setting by combining random projections with traditional data structures. In time series data mining, we devised a new space-efficient algorithm (SparseDTW) to compute the dynamic time warping (DTW) distance between two time series, which always yields the optimal result. This is in contrast to other approaches which typically sacrifice optimality to attain space efficiency. The main idea behind our approach is to dynamically exploit the existence of similarity and/or correlation between the time series: the more the similarity between the time series, the less space required to compute the DTW between them. Other techniques for speeding up DTW, impose a priori constraints and do not exploit similarity characteristics that may be present in the data. Our experiments demonstrate that SparseDTW outperforms these approaches. We discover an interesting pattern by applying SparseDTW algorithm: “pairs trading” in a large stock-market dataset, of the index daily prices from the Australian stock exchange (ASX) from 1980 to 2002
Geovisual analytics for spatial decision support: Setting the research agenda
This article summarizes the results of the workshop on Visualization, Analytics & Spatial Decision Support, which took place at the GIScience conference in September 2006. The discussions at the workshop and analysis of the state of the art have revealed a need in concerted cross‐disciplinary efforts to achieve substantial progress in supporting space‐related decision making. The size and complexity of real‐life problems together with their ill‐defined nature call for a true synergy between the power of computational techniques and the human capabilities to analyze, envision, reason, and deliberate. Existing methods and tools are yet far from enabling this synergy. Appropriate methods can only appear as a result of a focused research based on the achievements in the fields of geovisualization and information visualization, human‐computer interaction, geographic information science, operations research, data mining and machine learning, decision science, cognitive science, and other disciplines. The name ‘Geovisual Analytics for Spatial Decision Support’ suggested for this new research direction emphasizes the importance of visualization and interactive visual interfaces and the link with the emerging research discipline of Visual Analytics. This article, as well as the whole special issue, is meant to attract the attention of scientists with relevant expertise and interests to the major challenges requiring multidisciplinary efforts and to promote the establishment of a dedicated research community where an appropriate range of competences is combined with an appropriate breadth of thinking
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